Mau v2 – A template-based markup language written in Python

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Mau is a lightweight markup language heavily inspired by AsciiDoc that makes it a breeze to write blog posts or books. If you already know Markdown or AsciiDoc you already know which type of software Mau is, and you will quickly learn its syntax. The main goal of Mau, however, is to provide a customisable markup language. While Mau's syntax is fixed by its implementation, its output is created through user-provided templates. This strategy gives the user great flexibility with no added complexity. I currently use Mau to write posts for my blog The Digital Cat (https://www.thedigitalcatonline.com/) and to write my book Clean Architectures in Python 9https://leanpub.com/clean-architectures-in-python). The Mau documentation is also written using Mau. Source code: https://github.com/Project-Mau/mau Official documentation: The Mau book at https://www.thedigitalcatbooks.com/

Visit product View on Hacker NewsOct 20, 2021

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Indie HackersFits the IH revenue-focused audience · Strong signals: created · Missing: supports, reddit linkedin, podcasting

73%73% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

Hacker NewsStrong engagement from HN community · Strong signals: ide, io · Missing: https docs, excited, just released

54%54% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal

38%38% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Product HuntUnlikely to reach the leaderboard · Strong signals: user, using, code · Missing: mac, agents, macos

35%35% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive

31%31% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

18%18% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

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